Towards secure deep learning architecture for smart farming-based applications
- PDF / 1,167,404 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 43 Downloads / 223 Views
ORIGINAL ARTICLE
Towards secure deep learning architecture for smart farming-based applications R. Udendhran1
· M. Balamurugan1
Received: 1 September 2020 / Accepted: 26 October 2020 © The Author(s) 2020
Abstract The immense growth of the cloud infrastructure leads to the deployment of several machine learning as a service (MLaaS) in which the training and the development of machine learning models are ultimately performed in the cloud providers’ environment. However, this could also cause potential security threats and privacy risk as the deep learning algorithms need to access generated data collection, which lacks security in nature. This paper predominately focuses on developing a secure deep learning system design with the threat analysis involved within the smart farming technologies as they are acquiring more attention towards the global food supply needs with their intensifying demands. Smart farming is known to be a combination of data-driven technology and agricultural applications that helps in yielding quality food products with the enhancing crop yield. Nowadays, many use cases had been developed by executing smart farming paradigm and promote high impacts on the agricultural lands. Keywords Deep learning · Smart farming · Differential privacy · Image processing · Feature extraction · Convolutional neural networks · Gradient descent
Introduction Several agriculture farmers and researchers revamp to smart farming technology for determining soil condition and crops status at real time and also it could be used in sprinkling pesticides with the help of assisted drones, thereby protruding its multi-purposes [1]. On the other hand, the introduction of several communication modules and deep learning algorithms makes the system vulnerable to cyber-security [2] and threats in the smart farming infrastructure. This could lower the economy of a particular country, which predominately relies on the agricultural firm. Domain-specific problems, such as generated data, require privacy frameworks relevant to smart farming. Therefore, the implementation of smart farm technology needs more study prior to widespread community acceptance. For example, if any such information is used by rivals or aggressive actors, leakage of informa-
B
R. Udendhran [email protected] M. Balamurugan [email protected]
1
Department of Computer Science and Engineering, Bharathidasan University, Trichy, India
tion on the procurement of soil, crops, and agriculture will cause significant economic losses for farmers. Aggregating valuable agricultural information about a single nation on a wider scale is indeed a possible danger. While certain, data protection and privacy seem to be a very critical prerequisite for maintaining effective activity in a smart farming environment and is one of the key objectives. The differential privacy was introduced in the year 2006, and accepted as a de facto standard in preserving private data. Generally, differential privacy can be inferred with two settings: (1) global—real data are collected by a tru
Data Loading...
